The Impact of Flow Experience and Personality Type on the Intention to Use Virtual World

Virtual World (VW) had gained and lost its popularity over the past decades. Although people were initially excited about the potential of such technology, the original excitement has not been maintained and the VW has lost its reputation. We design an experimental study to learn about the factors that impact the intention to use or not use VW (i.e., Second Life, SL). Our proposed model is based on the Theory of Reasoned Action, Technology Acceptance Model, Embodied Social Presence theory, Flow theory, and Jungian personality theory. Over 160 students participated in this study and the results support our proposed model, where a positive flow experience with VW influences the attitude towards VW, in turn influencing intention to use VW. Furthermore, VW flow experience can itself be impacted by personality of the individuals behind the avatars in VW, perceived peer influence, perceived usefulness, and familiarity with VW.     

Impact of Traffic Management on Black Carbon Emissions: a Microsimulation Study

This paper investigates the effectiveness of traffic management tools, including traffic signal control and en-route navigation provided by variable message signs (VMS), in reducing traffic congestion and associated emissions of CO₂, NO_x, and black carbon. The latter is among the most significant contributors of climate change, and is associated with many serious health problems. This study combines traffic microsimulation (S-Paramics) with emission modeling (AIRE) to simulate and predict the impacts of different traffic management measures on a number traffic and environmental Key Performance Indicators (KPIs) assessed at different spatial levels. Simulation results for a real road network located in West Glasgow suggest that these traffic management tools can bring a reduction in travel delay and BC emission respectively by up to 6 % and 3 % network wide. The improvement at local levels such as junctions or corridors can be more significant. However, our results also show that the potential benefits of such interventions are strongly dependent on a number of factors, including dynamic demand profile, VMS compliance rate, and fleet composition. Extensive discussion based on the simulation results as well as managerial insights are provided to support traffic network operation and control with environmental goals. The study described by this paper was conducted under the support of the FP7-funded CARBOTRAF project.     

Network prefix-level traffic profiling: Characterizing,modeling, and evaluation

A cardinal prerequisite for the proper and efficient management of a network, especially an ISP network, is to understand the traffic that it carries. Traffic profiling is a means to obtain knowledge of the traffic behavior. Previous work has been focusing on traffic profiling at the link level or the host level. However, network prefix-level traffic behaviors have not yet been investigated. In this paper, we are interested in empirical studies for finding and describing structural patterns in the overwhelming network measurement data, as well as obtaining insight from it, with the expected traffic profiles potentially of interest to a broad range of applications such as network management, traffic engineering, and data services. To this end, first, we derive a collection of features that characterize the network prefix-level aggregate traffic behaviors. Next we use a simple model to capture them on all features, and apply machine learning techniques to extract representative profiles from them. Finally, we collect Netflow measurements from the entire periphery of a Tier-1 ISP network to empirically validate the simple model we proposed. Our extensive results show that nearly all networks exhibit traffic characteristics that are stable over time. The derived traffic profiles provide valuable insights on the manifold behavioral patterns that cannot be easily learned otherwise.     

Enterprise Network Traffic Monitoring, Analysis, and Reporting Using Web Technology

Today's enterprise networks are composed of multiple types of interconnected networks. Furthermore, organizations use a variety of systems and applications on these networks. Operations and management staff must provide an efficient, reliable and secure operating environment to support an organization's daily activities. Enterprise networks must be monitored for performance, configuration, security, accounting and fault management. Current management practices typically involve the use of complex, hard-to-learn and hard-to-use tools. What is needed desperately is a set of simple, uniform, ubiquitous tools for managing networks. Web-based management promises to provide such solutions. This paper focuses on the use of Web technology and the Multi-Router Traffic Grapher (MRTG) for the purposes of enterprise network traffic monitoring and reporting. In this paper, we first examine the requirements for enterprise network traffic monitoring, analysis and reporting, and then present the design and implementation of a Web-based network traffic monitoring and reporting system that satisfies those requirements. We also present guidelines we have formulated and used for analyzing enterprise network traffic. We then discuss our experiences in using such a system for traffic monitoring on two large enterprise networks.     

A self-adapting connection admission control solution for mobile WiMAX: Enabling dynamic switching of admission control algorithms based on predominant network usage profiles

WiMAX is a connection-oriented wireless network that provides QoS in metropolitan broadband communications. One important component in WiMAX QoS provisioning and management is the Connection Admission Control (CAC), which must be aware of the network conditions (e.g., user traffic demands and physical aspects). In our research, we define the association between a particular user traffic demand and a specific physical condition as a network usage profile. State-of-the-art proposals focus on optimizing CAC algorithms considering a single network usage profile; the adaptation of CAC algorithms when the predominant network usage profile changes is partially or fully neglected. In this article, we introduce a self-adapting CAC solution that, using a library of CAC algorithms, is able to switch the running algorithm according to the current network usage profile. The evaluation results, obtained through simulations, demonstrate that our self-adapting CAC solution is able to detect the changes on the predominant network usage profile. In addition, the results show how much different profiles can impact on the efficiency of CAC algorithms, thus confirming the need of switching the running CAC algorithm so that QoS can be guaranteed for the ongoing connections.     

A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application

Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.     

Applying interactive colour graphics in traffic planning

This paper considers the need for, and application of, interactive colour graphics in the planning and management of road traffic systems. It considers the use of computer models of road traffic networks and the advantages of graphics in displaying the distribution of traffic and its impacts over a study area. Examples are given of the use of interactive colour graphics in the four stages of model application: data input, model operations, output displays, and analysis of output data. The examples are taken from the study of a “local area” traffic network, and a range of alternative traffic management plans proposed and implemented in that area. Interactive graphics provides transportation engineers with the means to merge their needs for computer-based methods and models with their desires to use and produce maps and diagrams as a principal means of communication.     

Required extra capacity: A comparative estimation of overprovisioning needed for a classless IP backbone

The benefit of Class-of-Service (CoS) is an important topic in the “Network Neutrality” debate. As part of the debate, it has been suggested that over-provisioning is a viable strategy to meet performance targets of future applications, and that there is no need for to worry about provisioning differentiated services in an IP backbone for a small fraction of users needing better-than-best-effort service. In this paper, we quantify the extra capacity requirement for an over-provisioned classless (i.e., best-effort) network compared to a CoS network providing the same delay or loss performance for premium traffic. We first develop a link model that quantifies the required extra capacity (REC). To illustrate key parameters involved in analytically quantifying REC, we start with simple traffic distributions. Then, for more bursty traffic distributions (e.g., long-range dependent), we find the REC using ns-2 simulations of CoS and classless links. We, then, use these link models to quantify the REC for network topologies (obtained from Rocketfuel) under various scenarios including situations with “closed loop” traffic generated by many TCP sources that adapt to the available capacity. We also study the REC under link and node failures. We show that REC can still be significant even when the proportion of premium traffic requiring performance assurances is small, a situation often considered benign for the over-provisioning alternative. We also show that the impact of CoS on best-effort (BE) traffic is relatively small while still providing the desired performance for premium traffic.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

An anomaly-based detection in ubiquitous network using the equilibrium state of the catastrophe theory

It has been increasingly important for Pervasive and Ubiquitous Applications (PUA) of the network traffic, especially anomaly detection which plays a critical role in enforcing a high protection level of the network against threats. In this paper, we present a network traffic anomaly detection method based on the catastrophe theory. In order to characterize the normal behavior of the network, we construct a profile of the normal network traffic by using an equilibrium surface of the catastrophe theory. When anomalies occur, the state of the network traffic will deviate from the normal equilibrium surface. Then, taking the normal equilibrium surface as a reference, we monitor the ongoing network traffic and we use a new index called as catastrophe distance to quantify the deviation. According to the decision theory, network traffic anomalies can be identified by the catastrophe distance. We evaluate the performance of our approach using the DARPA intrusion detection data set. Experiment results show that our approach is significantly effective on the network traffic anomaly detection.     

自相似网络业务的一个FARIMA模型

近来发现,高速网络业务具有自相似及长相关特性,分数噪声可描述该类业务。但它仅表现长相关特性,给出了利用ＦＡＲＩＭＡ模型拟合自似网络业务的一整套方法,该模型同时刻画了实际业务的长相关与短相关行性,通过对实测数据的实验, 模型的优效性。     

Interactive Investigation of Traffic Congestion on Fat‐Tree Networks Using TreeScope

Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large‐scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents Tree Scope , an interactive, web‐based visualization tool for exploring network traffic on large‐scale fat‐tree networks. Tree Scope encodes the network topology using a tailored matrix‐based representation and provides detailed visualization of all traffic in the network. We report on the design process of Tree Scope , which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how Tree Scope 's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.     

Passive classification of wireless NICs during active scanning

Computer networks have become increasingly ubiquitous. However, with the increase in networked applications, there has also been an increase in difficulty to manage and secure these networks. The proliferation of 802.11 wireless networks has heightened this problem by extending networks beyond physical boundaries. We present a statistical analysis and propose the use of spectral analysis to identify the type of wireless network interface card (NIC). This mechanism can be applied to support the detection of unauthorized systems that use NICs that are different from that of a legitimate system. We focus on active scanning, a vaguely specified mechanism required by the 802.11 standard that is implemented in the hardware and software of the wireless NIC. We show that the implementation of this function influences the transmission patterns of a wireless stream that are observable through traffic analysis. Our mechanism for NIC identification uses signal processing to analyze the periodicity embedded in the wireless traffic caused by active scanning. A stable spectral profile is created from the periodic components of the traffic and used for the identity of the wireless NIC. We show that we can distinguish between NICs manufactured by different vendors, with zero false positives, using the spectral profile. Finally, we infer where, in the NIC, the active scanning algorithm is implemented.     

Algorithms for distributing traffic flows

We discuss models and algorithms of distributing traffic flows in the network of a large city or an algomeration. We provide comparative analysis of different algorithms for finding an equilibrium distribution in a transportation network. We discuss the problem of ambiguity in distributing correspondences by the paths in equilibrium and the use of entropy models to avoid this ambiguity.     

A proxy-based integrated cache consistency and mobility management scheme for client–server applications in Mobile IP systems

In this paper, we investigate a proxy-based integrated cache consistency and mobility management scheme for supporting client–server applications in Mobile IP systems with the objective to minimize the overall network traffic generated. Our cache consistency management scheme is based on a stateful strategy by which cache invalidation messages are asynchronously sent by the server to a mobile host (MH) whenever data objects cached at the MH have been updated. We use a per-user proxy to buffer invalidation messages to allow the MH to disconnect arbitrarily and to reduce the number of uplink requests when the MH is reconnected. Moreover, the user proxy takes the responsibility of mobility management to further reduce the network traffic. We investigate a design by which the MH’s proxy serves as a gateway foreign agent (GFA) as in the MIP Regional Registration protocol to keep track of the address of the MH in a region, with the proxy migrating with the MH when the MH crosses a regional area. We identify the optimal regional area size under which the overall network traffic cost, due to cache consistency management, mobility management, and query requests/replies, is minimized. The integrated cache consistency and mobility management scheme is demonstrated to outperform MIPv6, no-proxy and/or no-cache schemes, as well as a decoupled scheme that optimally but separately manages mobility and service activities in Mobile IPv6 environments.     

移动感知环境下基于CSA-SSVR的交通状态预测方法

相较于传统感知网络,移动群智感知网络在部署和维护成本上有着较大优势,在智能交通系统中得到了越来越多的应用。交通状态的预测对交通管理系统具有重要意义,从移动群智感知环境下获取的车速数据出发,以支持向量回归算法(SVR)为基础,引入周期性算子,并采用布谷鸟算法(CSA)确定周期性SVR(SSVR)中的主要参数,提出了CSA-SSVR,对道路未来车速进行预测,据此判断道路的未来交通状态。实验表明,CSA-SSVR在移动群智感知环境下对于交通状态预测问题的准确性较高。     

Processing intrusion detection alert aggregates with time series modeling

The main use of intrusion detection systems (IDS) is to detect attacks against information systems and networks. Normal use of the network and its functioning can also be monitored with an IDS. It can be used to control, for example, the use of management and signaling protocols, or the network traffic related to some less critical aspects of system policies. These complementary usages can generate large numbers of alerts, but still, in operational environment, the collection of such data may be mandated by the security policy. Processing this type of alerts presents a different problem than correlating alerts directly related to attacks or filtering incorrectly issued alerts.We aggregate individual alerts to alert flows, and then process the flows instead of individual alerts for two reasons. First, this is necessary to cope with the large quantity of alerts – a common problem among all alert correlation approaches. Second, individual alert’s relevancy is often indeterminable, but irrelevant alerts and interesting phenomena can be identified at the flow level. This is the particularity of the alerts created by the complementary uses of IDSes.Flows consisting of alerts related to normal system behavior can contain strong regularities. We propose to model these regularities using non-stationary autoregressive models. Once modeled, the regularities can be filtered out to relieve the security operator from manual analysis of true, but low impact alerts. We present experimental results using these models to process voluminous alert flows from an operational network.     

New MPLS network management techniques based on adaptive learning

The combined use of the differentiated services (DiffServ) and multiprotocol label switching (MPLS) technologies is envisioned to provide guaranteed quality of service (QoS) for multimedia traffic in IP networks, while effectively using network resources. These networks need to be managed adaptively to cope with the changing network conditions and provide satisfactory QoS. An efficient strategy is to map the traffic from different DiffServ classes of service on separate label switched paths (LSPs), which leads to distinct layers of MPLS networks corresponding to each DiffServ class. In this paper, three aspects of the management of such a layered MPLS network are discussed. In particular, an optimal technique for the setup of LSPs, capacity allocation of the LSPs and LSP routing are presented. The presented techniques are based on measurement of the network state to adapt the network configuration to changing traffic conditions.     

An optimal and stable feature selection approach for traffic classification based on multi-criterion fusion

There is significant interest in the network management community about the need to identify the most optimal and stable features for network traffic data. In practice, feature selection techniques are used as a pre-processing step to eliminate meaningless features, and also as a tool to reveal the set of optimal features. Unfortunately, such techniques are often sensitive to a small variation in the traffic data. Thus, obtaining a stable feature set is crucial in enhancing the confidence of network operators. This paper proposes an robust approach, called the Global Optimization Approach (GOA), to identify both optimal and stable features, relying on multi-criterion fusion-based feature selection technique and an information-theoretic method. The proposed GOA first combines multiple well-known FS techniques to yield a possible optimal feature subsets across different traffic datasets; then the proposed adaptive threshold, which is based on entropy to extract the stable features. A new goodness measure is proposed within a Random Forest framework to estimate the final optimum feature subset. Experimental studies on network traffic data in spatial and temporal domains show that the proposed GOA approach outperforms the commonly used feature selection techniques for traffic classification task.     

Traffic Engineering of Management Flows by Link Augmentations on Confluent Trees

Service providers rely on the management systems housed in their Network Operations Centers (NOCs) to remotely operate, monitor and provision their data networks. Lately there has been a tremendous increase in management traffic due to the growing complexity and size of the data networks and the services provisioned on them. Traffic engineering for management flows is essential for the smooth functioning of these networks to avoid congestion, which can result in loss of critical data such as billing records, network alarms, etc. As is the case with most intra-domain routing protocols, the management flows in many of these networks are routed on shortest paths connecting the NOC with the service provider’s POPs (points of presence). This collection of paths thus forms a “confluent” tree rooted at the gateway router connected to the NOC. The links close to the gateway router may form a bottleneck in this tree resulting in congestion. Typically this congestion is alleviated by adding layer two tunnels (virtual links) that offload the traffic from some links of this tree by routing it directly to the gateway router. The traffic engineering problem is then to minimize the number of virtual links needed for alleviating congestion. In this paper we formulate a traffic engineering problem motivated by the above mentioned applications. We show that the general versions of this problem are hard to solve. However, for some simpler cases in which the underlying network is a tree, we design efficient algorithms. In particular, we design fully polynomial-time approximate schemes (FPTAS) for different variants of this problem on trees. We use these algorithms as the basis for designing efficient heuristics for alleviating congestion in general (non-tree) service provider network topologies.